Top Machine Learning Jobs in India

Lead the design and implementation of AI/ML models, focusing on NLP, Generative AI, and MLOps while mentoring junior engineers.

As a Machine Learning Engineer at Level AI, you'll work on NLP challenges, develop scalable solutions, collaborate with teams, and innovate using cutting-edge ML techniques.

The Head of Machine Learning will manage a high-performing ML team, ensure the success of ML systems, and drive initiatives across business partnerships. The role emphasizes strong production engineering and theoretical ML expertise.

Lead the development of machine learning products by driving technical direction, designing data pipelines, and collaborating with teams to innovate solutions in consumer finance.

The role involves building and maintaining data systems, developing data pipelines, and collaborating with data scientists on machine learning models. Leadership responsibilities may include advising teams and guiding complex projects for operational effectiveness.

The Senior Machine Learning Engineer will design and optimize AI systems, fine-tune large-scale models, build workflows, and collaborate on innovative AI solutions.

You will design ML models to improve search and personalization, mentor junior engineers, and write maintainable code while collaborating with customers.

Develop ML solutions for decision-making using advanced modeling techniques. Collaborate with teams, design evaluations, and communicate results effectively. Strong coding skills and familiarity with ML tools required.

As a Senior Machine Learning Engineer, design, implement, and optimize AI components, collaborate across teams, and ensure the reliability of AI agents in fast-paced environments.

As a Junior Software Engineer specializing in AI/ML, you will assist in developing AI-driven applications, optimize Python/Node.js scripts, and enhance ML models for improved customer experience.

Develop and deploy recommender systems at eBay, focusing on AI models, big data pipelines, and enhancing user interaction with personalized recommendations.

Lead the architecture, development, and deployment of scalable ML and GenAI solutions. Oversee MLOps and collaborate with teams to enhance AI initiatives.

Lead the design and deployment of advanced machine learning models, mentor junior staff, and drive innovation in AI technology at Ema.

Develop and optimize machine learning models, manage project priorities, and improve real-world solutions using advanced ML techniques.

The Machine Learning Engineer IV will develop advanced algorithms for fraud detection in ID verification, ensuring security and enhancing user experience across various industries.

As an ML Engineer - Generalist, you'll enhance the Conversation AI platform, develop ML algorithms, optimize data workflows, and improve model performance.

The AI / ML Platform Engineer will build and maintain MLOps infrastructure on AWS, enabling scalable AI workflows and collaborating with data teams.

Lead the scaling of data platforms for robotics, managing data pipelines, ensuring model quality, mentoring engineers, and setting architectural direction.

The role involves designing and developing machine learning infrastructure for annotation, evaluation, and training models, focusing on scalable systems and efficient data workflows.

Design and develop machine learning infrastructure and models, support product teams, build scalable data services, and promote collaboration within a diverse engineering team.

Join Weave's Machine Learning Team as a Staff ML Engineer to design and develop AI infrastructure, build scalable services, and guide teams in leveraging data for application development.

As a Machine Learning Researcher, you'll lead research in applying ML to trading problems, develop deep learning models, and collaborate with quant researchers.

The role involves owning the MLOps infrastructure, migrating detection pipelines, building CI/CD pipelines, and ensuring system reliability in production.

Lead design and delivery of cloud-native AI applications, fine-tune large language models, establish evaluation standards, and partner with teams for AI features.

Develop AI and machine learning models for healthcare data analysis, including anomaly detection. Collaborate with teams to integrate solutions and ensure compliance with standards.